sf-homelessness.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Correlation between SF rental rate and homelessness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
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     "name": "stdout",
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     "text": [
      "r_value: 0.373609695268\n",
      "p_value: 0.257710359871\n",
      "\n",
      "r_value2: 0.432365769157\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import requests\n",
    "import scipy.stats\n",
    "\n",
    "resp = requests.get(\"https://numeracy.co/projects/5S3mIklNmpw/sf-rentals-by-district.csv\")\n",
    "\n",
    "rows = csv.DictReader(resp.text.splitlines())\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "y2 = []\n",
    "for row in rows:\n",
    "    x.append(float(row[\"% rentals\"]))\n",
    "    y.append(float(row[\"number of homeless\"]))\n",
    "    y2.append(float(row[\"homeless per sq mile\"]))\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
    "slope2, intercept2, r_value2, p_value2, std_err2 = scipy.stats.linregress(x, y2)\n",
    "\n",
    "print \"r_value: \" + str(r_value)\n",
    "print \"p_value: \" + str(p_value)\n",
    "\n",
    "print \"\\nr_value2: \" + str(r_value2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
   "source": []
  }
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